Demand Prediction

Foresee the future of your business

From challenges to solutions
Challenge

Business decisions heavily depend on expectations about the future. Such predictions are subjective and depend on the experience of an expert.

Idea

We use Machine Learning to gather predictions that are purely based upon data.

Solution

We prepare several sources of data relevant to the domain, train a model upon past observations, and let it then predict future developments.

Challenge

Business strategies and decisions rely on certain assumptions about markets, customer behavior, etc. In real-world domains we have experts with their knowledge and experience as well as adequate tools to draw conclusions and make predictions. Disadvantages are that such experts are expensive, not always available, and after all not always right in their subjective view of the future.

Idea

We apply Machine Learning methods to train models of a certain domain and then use these models for predicting. The result is more objective in comparison to experts’ judgements. Also, predictions can be computed continuouly as input to live systems. Another common application is the configuration of alarms based upon forecasts to allow for some response time.

Or a route and dolly proposal by an intelligent system that takes into account all these factors and others as the task deadlines of you and your peers or the predicted availability of assets at locations?

Solution

With our Machine Learning approach, we rely on historicy data and past observations. We analyze the relevance of the data at hand to the prediciton target, influencing factors, correlations, and patterns. Our methods comprise visualization and statistics as well as complex model training algorithms.

In addition to this purely data-driven approach, known and proven interrelations or constraints can be integrated as features to boost the model.

Predictive Maintenance:

Maintenance is often conducted in a conservative way, i.e. performing it often enough to reduce the risk of demages to an acceptable level. With prediction models the end-of-life of machine components can be estimated. With this information, operators can save costs by reducing maintenance tasks to points in time where they are indeed neccessary. On the other hand, they can plan down-times with respect to constraints and optimization goals.

Reference Projects:
  • Steyr Motors
Mobility / Taxi Demand Prediction:

Taxis are an integral part of transport systems around the world. However, the profit per ride is relatively small and thereby the level of utilization is one crucial factor for competitive advantage and business success in this industry. Thereby operators of a taxi fleet have a strong demand to optimise the positioning (and guidance) of taxis of the fleet.

Reference Projects:
Energy Demand Prediction:

In modern energy supply systems locally available resources like wind, solar, or water do have a significant share. They disrupt the traditional centralized power generation model, giving way to a complex and dynamic system with multiple players and energy sources. As a  consequence, the energy market is becoming highly dynamic, with energy producers having the flexibility to decide when it is better to store, use or sell the energy they produce. In this context energy demand forecasts become crucial as they can bring large savings for both consumers and producers.

Reference Projects:
Vision
Wanna foresee the future?
You have a current pain point?

Get in tough with us to discuss the technical solution.

You see the potential of prediction as AI technology?

Contact us for your personalized “Get inspired by Catalysts” event

Your contact person

Bernhard Niedermayer

Segment Lead Emerging Technologies

bernhard.niedermayer@catalysts.cc

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